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ICASSP
2008
IEEE
14 years 3 months ago
Discriminative feature selection for hidden Markov models using Segmental Boosting
We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying featur...
Pei Yin, Irfan A. Essa, Thad Starner, James M. Reh...
BMCBI
2010
133views more  BMCBI 2010»
13 years 9 months ago
Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction
Background: With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and appl...
Lance E. Palmer, Mathäus Dejori, Randall A. B...
COLING
1992
13 years 10 months ago
Syntactic Ambiguity Resolution Using A Discrimination and Robustness Oriented Adaptive Learning Algorithm
In this paper, a discrimination and robusmess oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution. Owing to the problem of ins...
Tung-Hui Chiang, Yi-Chung Lin, Keh-Yih Su
BMCBI
2008
139views more  BMCBI 2008»
13 years 9 months ago
The C1C2: A framework for simultaneous model selection and assessment
Background: There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper m...
Martin Eklund, Ola Spjuth, Jarl E. S. Wikberg
BMCBI
2010
138views more  BMCBI 2010»
13 years 9 months ago
Sigma-2: Multiple sequence alignment of non-coding DNA via an evolutionary model
Background: While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a ...
Gayathri Jayaraman, Rahul Siddharthan